Continuous physiological monitoring can play a crucial role in finding and treating asymptomatic pathologies in patients before they become life threatening. Examples of useful physiological data that can be collected and analyzed include electrocardiograms (EKG), blood oxygen levels, weight, blood pressure and many others.
In a typical continuous monitoring system, patients wear devices that collect data of interest continuously and the data is aggregated and transmitted to a remote host for further analysis. Settings like this are of particular interest for diseases like Atrial Fibrillation (Afib) which are both asymptomatic and intermittent. Continuous monitoring using wearable EKG's can provide the information necessary to diagnose and treat the disease.
Unfortunately, in the typical monitoring environment, a variety of factors conspire to reduce the quality of the signal. Noise due to patient mobility, packet loss due to wireless problems, aggregating device buffer overruns and other problems result in losing some fraction of the data being collected by the device. There is a need in a method and a device for detecting atrial fibrillation events in a signal transmitted in a lossy data stream.